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UK accident and emergency departments have to treat patients from arrival to discharge, transfer or admission within four hours. Scientific methods to determine the triage category are important for the hospital to effectively manage resources and staff. In this paper, two clustering algorithms, i.e., K-means and fuzzy c-means clustering, are applied to establish the triage category via data collected...
This paper proposes a methodology for finding typical load profiles for residential customers by using clustering techniques. Such task is particularly challenging due to the great diversity of electricity use by residential customers. Specific characteristics of this kind of customers, as number of inhabitants or house surface, may help the clustering, but such features are often, maybe always, unknowable...
In modern remote sensing procedures, one of the most important issues is to distinguish specific types of land coverage. Discrimination between different land coverages especially in metropolitan surveying is so important that the in front civilization projects are basically dependent to them. In this paper, an innovative image processing strategy is employed for distinguishing green lands from other...
Estimating the initial background of a scene is a key prerequisite for several applications in video analytics. In this paper, we present a simple approach that takes into account spatio-temporal motion intensities while estimating the true background. We tested the algorithm on real video sequences from the Scene Background Initialization (SBI) benchmark dataset, and the results show that the algorithm...
Clustering techniques have gained great popularity in neuroscience data analysis especially in analysing data from complex experiment paradigm where it is hard to apply traditional model-based method. However, when employing clustering analysis, many clustering algorithms are available nowadays and even with an individual clustering algorithm, choices like parameter settings and distance metrics are...
The article focuses on the results of the research into scientific publications of the All-Russian Institute for Scientific and Technical Information of the Russian Academy of Sciences database (VINITI Database RAS) in different fields. The purpose of operation was to increase partition accuracy on the directions of large volumes of scientific data. This analysis was carried out on summaries of scientific...
Data Mining is the process of analyzing large amount of data and useful for knowledge discovery. Detection of outliers is critically essential in the knowledge based society. Focusing on outlier detection in offline data stream has been increased in the past few years. The proposed a new CLOPD algorithm for identifying mislabelled data (anomaly) during clustering and increase the accuracy of cluster...
Fault diagnosis plays a crucial role to maintain healthy conditions in rotating machinery. This paper proposes a framework to detect new patterns of abnormal conditions in gearboxes, that would be associated to new faults. This is achieved through a Hybrid Heuristic Algorithm for Evolving Models in scenarios of Classification and Clustering (HHA-EMCC), which is a machine learning algorithm that can...
Conventional semi-supervised clustering approaches have several shortcomings, such as (1) not fully utilizing all useful must-link and cannot-link constraints, (2) not considering how to deal with high dimensional data with noise, and (3) not fully addressing the need to use an adaptive process to further improve the performance of the algorithm. In this paper, we first propose the transitive closure...
The focus of this paper is on detecting overlapping communities for the directed graphs by implementing a new algorithm and analyzing it with various performance metrics. The algorithm aims at finding core nodes for the directed graph which are subset of communities and have higher contact frequency. These are then extended to find communities using compactness measurement (CM). The compactness of...
K Nearest Neighbor Join (KNN Join) is a primitive operation widely adopted by many data mining applications. As a combination of the k nearest neighbor query and the join operation, KNN Join is a computationally intensive algorithm; however, with the increase of data volume and data dimension, the results can't be obtained within acceptable time when this algorithm runs on a single machine. Consequently,...
We present a novel approach for phase denoising in Interferometric Synthetic Aperture Radar (InSAR) images, named as Block-Matching InSAR (BMInSAR). It uses k-means clustering to solve the block matching similarity search problem, thus simplifying preprocessing steps and filtering several reference-blocks at once. Also, we propose a novel methodology based on ground-truth GPS measurements to assess...
In complex networks, communities often show the presence of homophily between members, since homophily is the tendency of individuals to associate and bound with similar others to form densely interconnected groups. In this paper, we propose a new bidirectional label propagation community detection algorithm, called Dis-Sim, based on the fundamental idea that a node and its most similar neighbors...
In the literature, there are many clustering algorithms proposed for the vehicle ad hoc networks (VANETs) to improve network stability and scalability. However, there is a lack of comprehensive comparison among them. In this paper, we show that there exists unfair comparison of clustering algorithms, in the aspect of simulators, performance metrics, simulation scenarios, and configuration of algorithms...
In order for autonomous surface vessels (ASVs) to avoid collisions at sea it is necessary to predict the future trajectories of surrounding vessels. This paper investigate the use of historical automatic identification system (AIS) data to predict such trajectories. The availability of AIS data have steadily increased in the last years as a result of more regulations, together with wider coverage...
Partitioning of electric networks into zones or areas is a procedure that has numerous applications in power system planning, operation and control. Spectral clustering based approaches are among the most favoured ones to solve the partitioning problem. Applications of spectral clustering include definition of control zones, analysis of connectivity structure of power networks, intentional controlled...
We present a new algorithm for discovering clusters in noisy data streams using dynamic and cluster-specific temporal decay factors. Our improvement helps identify and adapt to evolving trends by adapting the weighting of stream data based on both content attributes and temporal arrival patterns. Our experimental results show that the proposed algorithm can discover better quality clusters in noisy...
Radio environment maps can be a powerful tool for achieving efficient context-aware resource allocation in 5G heterogeneous networks. In this paper, we consider an heterogeneous network formed by a traditional cellular network and a wireless sensor network. The role of the wireless sensor network is to estimate the radio environment map of the cell using a geostatistical interpolation technique named...
Outlier detection has been shown to be a promising machine learning technique for a diverse array of felds and problem areas. However, traditional, supervised outlier detection is not well suited for problems such as network intrusion detection, where proper labelled data is scarce. This has created a focus on extending these approaches to be unsupervised, removing the need for explicit labels, but...
With continuously growing data, clusters also need to grow periodically to accommodate the increased demand of data processing. This is usually done by addition of newer hardware, whose configuration might differ from the existing nodes. As a result, clusters are becoming heterogeneous in nature. For many real world machine learning and data mining applications, data is represented in the form of...
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